System of Temperature Stabilization in a Heating Furnace Based on Sliding Mode Control and Fuzzy Logic

The paper addresses issues of temperature control in heating furnaces using the example of a strand-type furnace for steel strip annealing of a continuous hot-dip galvanizing unit. The authors demonstrate that one of the main problems is variability of dynamic properties in the controlled object upon flexible production management with changes in the furnace capacity. Using a model of thermal state of the furnace cavity and metal, considering the impact of furnace temperature conditions on heat losses, variation limits of the parameters in a simplified model of object dynamics are determined. Issues of controlling a temperature object with variable dynamic parameters are reviewed. Advantages and disadvantages of systems used to control such objects, based on fuzzy logic and sliding mode control, are studied. It is shown that, in controlling a temperature object, a sliding mode control system can lead to fluctuating transient responses due to the absence of amplitude modulation in the control action when approaching the set-point. The authors suggest a system for automatic stabilization of the controlled parameter of a temperature object where sliding mode control and fuzzy logic are combined. In the stabilization system suggested, the direction of changes in the control action is determined using sliding mode control, while the control action level is determined using fuzzy logic. The paper provides results of simulation experiments comparing the efficiency of control using the system suggested and efficiency of control using the system based only on fuzzy logic. During those experiments, optimal system setting parameters were determined using complete enumeration and computer simulation of control for an object with the set variation of dynamic properties. Computer simulation was performed in the VisSim environment. The paper also shows that, with constant values of signal scaling parameters used in fuzzy logic, the requirement for qualitative transient responses with various set-point change levels results in the significantly reduced response speed in comparison with the system that combines fuzzy logic and sliding mode control. The authors demonstrate that it is possible to adjust quality of transient responses in the system that combines fuzzy logic and sliding mode control, changing dynamic properties of the object.

heating furnace, flexible management, capacity, automatic control, temperature, sliding mode control, fuzzy logic

1. Yong Yin, Kathryn E. Stecke & Dongni Li. The evolution of production systems from Industry 2.0 through Industry 4.0, International Journal of Production Research, no. 56:1-2, pp. 848—861.

2. Ryabchikov M. Yu. Adaptation of strand-type tower furnace heating simulation and of metal heating for steel strip annealing heating modes management, Control Sciences, 2017, no. 5, pp. 61—69 (in Russian).

3. Ryabchikov M. Yu., Samarina I. G. Studying of strip heating dynamics in the tower type furnace, Metallobrabotka, 2013, no. 1 (73), pp. 43—49 (in Russian).

4. Savin D. V., Drozdov V. G. Modern approach to systems automatic control heating building, Tekhnicheskie nauki — ot teorii k praktike, 2014, no. 30, pp. 51—56 (in Russian).

5. Finaev V. I., Sinyavskaya E. D., Pushnina I. V. Fuzzy control model of temperature at the bread baking chamber, IZVESTIYA SFedU. ENGINEERING SCIENCES, 2015, no. 4 (165), pp. 149—159 (in Russian).

6. Xiaojing Gao, Yan Ma, Hong Chen. Active Thermal Control of a Battery Pack Under Elevated Temperatures, IFAC PapersOnLine, 2018, no. 51-31, pp. 262—267.

7. Pilyae v S. N., Afonichev D. N. Energy saving automatic control system for the process of aeration of grain, Aktual’nye napravleniya nauchnyh issledovanij XXI veka: teoriya i praktika, 2015, vol. 3, no. 5-4, pp. 140—144 (in Russian).

8. Jin Woo Moon, Jin Chul Park, Sooyoung Kim. Development of control algorithms for optimal thermal environment of double skin envelope buildings in summer, Building and Environmen, 2018, no. 144, pp. 657—672.

9. Ahmad Esmaeilzadeh, Mohammad Reza Zakerzadeh, Aghil Yousefi Koma. The comparison of some advanced control methods for energy optimization and comfort management in buildings, Sustainable Cities and Society, 2018, no. 43, pp. 601—623.

10. Hizhnyakov Yu. N. Fuzzy control of the heat-carrier temperature, PNRPU Bulletin. Electrotechnics, Informational Technologies, Control Systems, 2016, no. 20, pp. 5—12 (in Russian).

11. Shtepa V. N., Prokopenya O. N., Kot R. E., Puha V. M. The microprocessor control system of the dosing reagents based on the fuzzy logic, Vestnik Brestskogo gosudarstvennogo tekhnicheskogo universiteta. Mashinostroenie, 2015, no. 4 (94), pp. 60—64 (in Russian).

12. Lubencova E. V., Volodin A. A., Lubencov V. F. Neurofuzzy control system for temperature control fermentation, Information Technology, 2014, no. 3, pp. 55—62 (in Russian).

13. Ryabchikov M. Yu., Ryabchikova E. S. Self-tuning of a neural network controller with an integral estimate of contradictions between the commands of the learning algorithm and memory, Automation and Remote Control, 2018, no. 2, pp. 154—166 (in Russian).

14. Pol’ko P. G., Logunova O. S., Andreev S. M., Ryabchikova E. S., Ryabchikov M. Yu., Parsunkin B. N. Fuzzy control algorithm for synthesis of automatic stabilization systems, Automation in Industry, 2010, no. 11, pp. 32—37 (in Russian).

15. Novikov S. I., Shahnovich V. R., Safronov A. V. Fuzzy Logic Methods in the Tasks of Thermal Processes Automation of Power Plant, Vestnik IGEU, 2010, no. 4, pp. 1—4 (in Russian).

16. Cherneckaya I. V., Cherneckij V. O. Fuzzy regulators in automatic control systems, Bulletin of the South Ural State University, 2006, no. 14, pp. 156—159 (in Russian).

17. Lei Hang, Do-Hyeun Kim. Enhanced Model-Based Predictive Control System Based on Fuzzy Logic for Maintaining Thermal Comfort in IoT Smart Space, Appl. Sci., 2018, no. 8, p. 1031.

18. Filimonov A. B., Filimonov N. B. To the question of constructing fuzzy PID regulators, Mekhatronika, avtomatika i robototekhnika, 2018, no. 2, pp. 112—116 (in Russian).

19. Suman Debnath, Jagannath Reddy, Jagadish and Biplab Das. Investigation of thermal performance of SAC variables using fuzzy logic based expert system, Journal of Mechanical Science and Technology, 2019, vol. 33, no. 8, pp. 4013—4021.

20. Kudinov Yu. I., Dorohov I. N., Pashchenko F. F. Fuzzy controllers and control systems, Control Sciences, 2004, no. 3, pp. 2—14 (in Russian).

21. Meysam Gheisarnejad, Mohammad Hassan Khooban. Design an optimal fuzzy fractional proportional integral derivative controller with derivative filter for load frequency control in power systems, Transactions of the Institute of Measurement and Control, 2019, pp. 1—19.

22. Ivajkin V. Application of Sliding Mode in the Automatic Systems, Contemporary Technologies in Automation, 2006, no. 1, pp. 90—94 (in Russian).

23. Filimonov A. B., Filimonov N. B. Sertain Problematic Aspects of Fuzzy PID Regulation, Mekhatronika, Avtomatizatsiya, Upravlenie, 2018, vol.19, no. 12, pp. 762—769 (in Russian).

24. Ryabchikov M. Yu., Parsunkin B. N., Andreev S. M., Polyko P. G. Logunova O. S., Ryabchikova E. S., Golovko N. A. Maximal efficiency fuzzy logic based extremal control system, Vestnik of Nosov Magnitogorsk State Technical Unive rsity, 2011, no. 4, pp. 65—69 (in Russian).